Question 1

In vegetation remote sensing, how does a reduction in nitrogen nutrient status of plants manifest in spectral and visual indicators (leaf colour, pigment concentration, and reflectance)?

Accepted Answer

Introduction / Context: Nitrogen is a primary plant nutrient and a key component of chlorophyll and proteins. In remote sensing and precision agriculture, nitrogen deficiency produces reproducible spectral signatures and visible symptoms that can be diagnosed from field observations and multispectral imagery. This question checks your understanding of how low nitrogen affects leaf colour, pigment levels, and reflectance in the visible bands. Given Data / Assumptions: Plants are experiencing a reduction in available nitrogen. We consider typical healthy foliage versus nitrogen-stressed foliage. Observations include both visual inspection and spectral reflectance in the visible domain. Concept / Approach: Chlorophyll concentration drives strong absorption in blue and red wavelengths and gives healthy leaves their deep green tone. When nitrogen is deficient, chlorophyll formation declines, carotenoids may become relatively more apparent, and overall pigment pools drop, changing both colour and reflectance. The classic spectral effect is higher reflectance in the red band (due to reduced absorption) and paler green appearance, often termed chlorosis. Step-by-Step Solution: Link nitrogen to chlorophyll synthesis: less nitrogen → less chlorophyll.Lower chlorophyll reduces pigment concentration across chlorophylls and often accessory pigments.Reduced absorption in the visible, especially red, increases reflectivity (brightness) in those bands.Visually, leaves shift from deep green toward yellowish or pale green (colour change).Therefore, all the listed effects occur under nitrogen reduction. Verification / Alternative check: Vegetation indices like the Red-Edge indices and even simple NDVI may decline with nitrogen stress; reflectance curves show elevated red reflectance and a weakened red-edge position as chlorophyll decreases. Why Other Options Are Wrong: Each single statement captures only part of the response; nitrogen stress simultaneously changes colour, pigment concentration, and visible reflectivity, so the comprehensive choice is correct. Near-IR reflectance may change but is less directly tied to nitrogen than visible absorption by chlorophyll; stating only NIR effects is incomplete. Common Pitfalls: Confusing water stress with nitrogen stress; assuming all spectral bands change equally; ignoring that leaf structure largely governs near-IR while pigments dominate visible absorption. Final Answer: All of these

Question 2

Remote sensing of Earth's surface commonly exploits multiple atmospheric transmission windows. Which wavelength ranges (μm or cm) are typically used for optical/IR and microwave Earth observation?

Accepted Answer

Introduction / Context: Earth observation sensors are designed around atmospheric windows—spectral regions where absorption by gases like water vapour and carbon dioxide is relatively low. Optical and infrared instruments occupy several windows, while microwave (centimetre wavelengths) systems such as radar exploit broader all-weather transmission bands. This question asks you to identify the commonly used bands across these regions. Given Data / Assumptions: Spectral ranges listed include visible–near IR, shortwave IR, mid–thermal IR, and microwave windows. We are considering typical, widely used ranges rather than an exhaustive list. Units are micrometres (μm) for optical/IR and centimetres (cm) for microwave. Concept / Approach: Window sets include: visible to near-IR (approx. 0.4–1.3 μm), shortwave IR (e.g., 1.5–1.8 μm and 2.2–2.6 μm), mid-IR (around 3–5 μm), thermal IR (about 8–14 μm within the larger 7–15 μm region), plus microwave windows in the centimetre range (for example, 1–30 cm covering L, S, C, X, Ku bands used by SAR and scatterometers). Because each option lists a valid window group, the correct selection is the inclusive one. Step-by-Step Solution: Recognize that 0.4–1.3 μm and the two SWIR windows are standard for multispectral imagers.Identify 3–5 μm and 7–15 μm as mid/thermal IR regions where emissive sensing occurs.Note that 1–30 cm corresponds to radar/microwave sensing through clouds and at night.Hence, all listed ranges are used, so “All of these.” Verification / Alternative check: Band placements on sensors like Landsat, Sentinel-2/3, MODIS, VIIRS, and SAR missions confirm these window selections. Why Other Options Are Wrong: Any single set alone would omit other widely used windows; therefore only the combined option reflects practice. Restricting to a narrow visible band (e.g., only 0.6–0.7 μm) ignores critical IR and microwave capabilities. Common Pitfalls: Mixing micrometres and centimetres; forgetting that strong absorption bands (e.g., around 1.4, 1.9 μm) are usually avoided in reflective imaging. Final Answer: All of these

Question 3

Wave fundamentals in remote sensing/physics: choose the correct statements about wavelength (λ) and amplitude for a travelling wave.

Accepted Answer

Introduction / Context: Many remote sensing concepts—from electromagnetic radiation to acoustic and radar signals—rely on basic wave terminology. A clear understanding of the definitions of wavelength and amplitude is essential for interpreting sensor specifications and spectral responses. Given Data / Assumptions: A sinusoidal travelling wave is assumed for clarity. Standard physics notation is used: λ for wavelength. Amplitude refers to the peak displacement relative to the mean position. Concept / Approach: Wavelength is the spatial period of a wave, the distance over which the wave's shape repeats (crest-to-crest or trough-to-trough). Amplitude is the maximum magnitude of displacement from the equilibrium (midline) to the crest (or trough). These definitions underpin spectral resolution (related to λ) and signal strength considerations in sensors. Step-by-Step Solution: Match definition: consecutive crests/troughs → wavelength.Units: metres and submultiples are standard for spatial distance.Symbol: λ is the universally accepted notation for wavelength.Amplitude: distance from midline to crest (or trough) is the correct definition.Hence, all listed statements are correct. Verification / Alternative check: Reference wave equations confirm that period in space equals wavelength; amplitude appears as the coefficient controlling peak displacement. Why Other Options Are Wrong: The alternatives are components of the full definition; rejecting any one would be incomplete. Common Pitfalls: Confusing amplitude with total wave height (crest-to-trough is 2 * amplitude); mixing time period with wavelength. Final Answer: All of these

Question 4

Target identification in remote sensing relies on multiple distinguishing attributes. Which variations are important for recognizing features reliably?

Accepted Answer

Introduction / Context: Feature extraction and classification in remote sensing leverage several complementary cues. No single attribute is sufficient in all cases; instead, robust identification combines spectral, spatial, temporal, and polarization information where available, improving accuracy and reducing confusion between classes. Given Data / Assumptions: Sensors may provide multispectral or hyperspectral bands (spectral), high spatial detail (spatial), multi-date coverage (temporal), and in radar, selectable polarization (polarimetric). Targets can be vegetation, water, soil, urban materials, or snow/ice. Concept / Approach: Spectral signatures differentiate materials by reflectance/emittance vs wavelength. Spatial cues (shape, texture, pattern) separate objects with distinct geometries. Temporal profiles capture phenology, flooding cycles, or construction. Polarization behavior, especially in SAR, reveals surface roughness and orientation. Combining these dimensions forms a richer feature space for classification and interpretation. Step-by-Step Solution: Identify candidate attributes relevant to targets.Note how each attribute resolves specific ambiguities (e.g., crops by temporal phenology).Conclude that all listed variations improve identification when jointly considered. Verification / Alternative check: Operational land-cover products often fuse spectral indices, textural measures, time-series metrics, and SAR polarimetric parameters to achieve higher accuracy. Why Other Options Are Wrong: Choosing any single attribute ignores the benefits of multi-dimensional feature spaces. Common Pitfalls: Expecting spectral information alone to resolve look-alikes; neglecting seasonality or radar polarization that can discriminate difficult classes. Final Answer: All of these

Question 5

For an Earth-observation sensor system to be useful, which fundamental resolutions must be considered as basic requirements?

Accepted Answer

Introduction / Context: Sensor performance is characterized by multiple resolutions that jointly determine data quality and applicability. Neglecting any one dimension can hinder the ability to detect targets, separate classes, or monitor change effectively. Given Data / Assumptions: We assess four core resolutions: radiometric, spatial, spectral, and temporal. Application contexts may emphasize different priorities (e.g., agriculture versus disaster response). Concept / Approach: Radiometric resolution affects the sensor's sensitivity to brightness differences. Spatial resolution governs the smallest discernible feature. Spectral resolution defines which materials can be distinguished by their spectral signatures. Temporal resolution controls how often a location can be reimaged for change detection. Effective mission design balances these constraints to meet user needs and platform limits. Step-by-Step Solution: List the four key resolutions and their roles.Recognize that each is essential to at least one critical use-case.Conclude that all are basic requirements. Verification / Alternative check: Product specifications for missions like Landsat, Sentinel, and MODIS highlight all four resolutions, demonstrating their fundamental status. Why Other Options Are Wrong: Omitting any resolution will undermine some applications (e.g., poor temporal resolution fails time-critical monitoring). Common Pitfalls: Overemphasizing spatial resolution while ignoring spectral and temporal needs; confusing radiometric resolution with dynamic range calibration alone. Final Answer: All of these

Question 6

Differential GPS (DGPS) performance: which statement best represents typical position accuracies as a function of rover distance from the reference station?

Accepted Answer

Introduction / Context: Differential GPS (DGPS) corrects many common-mode errors by referencing a fixed base station. Accuracy depends on baseline length (distance from rover to reference), ionospheric conditions, and whether code-based or carrier-phase techniques are used. This question targets typical code-based DGPS performance over short baselines. Given Data / Assumptions: Standard real-time code-based DGPS corrections. Short baselines in the 1–2 km range with good geometry. No advanced RTK carrier-phase ambiguity resolution (which can reach centimetre level). Concept / Approach: As the rover moves farther from the reference, spatial decorrelation of errors (ionosphere, troposphere) reduces the effectiveness of differential corrections, degrading accuracy. Over very short baselines, DGPS commonly achieves around 1–2 m horizontal accuracy. Larger baselines (tens of kilometres) show several metres of error, unless more sophisticated techniques (e.g., RTK, network RTK) are used. Step-by-Step Solution: Identify the code-based DGPS accuracy regime (metre-level).Note best performance at short baselines ~1–2 km: ~1–2 m accuracy.Recognize accuracy degrades with longer baselines to several metres.Therefore, select the statement describing ~1–2 m at ~1–2 km. Verification / Alternative check: Manufacturer datasheets and field results commonly cite ~1–3 m DGPS accuracy under good conditions for short baselines, improving to sub-metre only with augmentation or carrier-phase techniques. Why Other Options Are Wrong: 2–5 m at 2–5 km or 5–10 m at 5–10 km/25 km represent degraded performance relative to best-case short baselines. 0.1 m without carrier-phase is unrealistic for code-based DGPS. Common Pitfalls: Conflating DGPS with RTK; ignoring baseline dependence; assuming advertised best-case values apply at all distances. Final Answer: Positional accuracies ~ 1 – 2 m if rover is less than 1–2 km from the reference station

Question 7

GPS receiver processing flow: choose the correct sequence from signal capture to final user output.

Accepted Answer

Introduction / Context: A GPS receiver must acquire and track satellite signals, decode navigation data, process observations, and present a position/velocity/time (PVT) solution to the user. Understanding this pipeline clarifies where errors and delays may occur and how augmentation like DGPS fits in. Given Data / Assumptions: Stage 2: Detection of the GPS signal (acquisition/tracking). Stage 4: Decoding the GPS signal (navigation message). Stage 3: Processing in the built-in computer (solution estimation). Stage 1: Generation of output to the user (final PVT). Concept / Approach: Receivers first detect and lock onto the satellite carrier and code, then decode the navigation message to obtain ephemerides and timing models. Next, they process the pseudorange and, if available, carrier-phase measurements to estimate position/time. Finally, they output the solution to the user interface or data port. Step-by-Step Solution: Start with signal detection: 2.Decode navigation data: 4.Compute solution in firmware/software: 3.Provide user output: 1.Therefore, the correct sequence is 2, 4, 3, 1. Verification / Alternative check: Receiver ICDs and textbooks outline acquisition → tracking → data demodulation → navigation solution → output, which aligns with this order. Why Other Options Are Wrong: Any sequence that outputs before decoding/processing is illogical. Processing cannot precede decoding of necessary ephemerides/time models. Common Pitfalls: Confusing acquisition (signal lock) with data decoding; assuming solutions can be computed without valid ephemerides. Final Answer: 2, 4, 3, 1

Question 8

Snow optics: how does increasing snow grain size affect albedo across wavelengths, and in which spectral region is this effect strongest?

Accepted Answer

Introduction / Context: Snow albedo critically influences surface energy balance and melt. Remote sensing retrievals of snow properties rely on predictable changes in reflectance with grain size, impurities, and liquid water content. This question focuses on the canonical effect of grain coarsening on albedo across wavelengths. Given Data / Assumptions: We compare fine-grained fresh snow to coarser, metamorphosed snow. Wavelength regions considered: visible (VIS) and near-infrared (NIR). No soot/dust contamination; effect attributed primarily to grain size. Concept / Approach: As snow grains become larger, the path length of photons within the ice matrix increases, raising the probability of absorption (particularly in NIR where ice absorption is stronger). Consequently, albedo decreases with grain growth across most bands, but the drop is most pronounced in near-IR; visible bands are less sensitive because absorption by pure ice is weak there, so reflectance remains relatively high though it can still decline modestly. Step-by-Step Solution: Recognize the general trend: increasing grain size → lower albedo.Identify spectral dependence: strongest decline in NIR due to higher ice absorption.Visible response is weaker but still negative; hence “low in visible.”All three statements are simultaneously true, so choose “All of these.” Verification / Alternative check: Snow reflectance spectra show steep NIR decreases as grains grow; algorithms such as NDSI leverage visible–NIR contrast. Why Other Options Are Wrong: Claiming only VIS increases contradicts physical absorption behaviour. Common Pitfalls: Attributing albedo changes solely to impurity content; overlooking the spectral variability of ice absorption coefficients. Final Answer: All of these

Question 9

Wave phase: select the correct statements about how phase is defined, referenced, and commonly expressed for a periodic wave.

Accepted Answer

Introduction / Context: Phase describes the relative position within a wave cycle and is fundamental to understanding interference, coherence, and time alignment in remote sensing signals (e.g., SAR interferometry) and signal processing in general. Given Data / Assumptions: A periodic signal and a consistent reference point are assumed. Phase can be represented in degrees or radians. Zero-crossings are common practical references for phase measurements. Concept / Approach: Phase quantifies how far a point is through its cycle relative to a reference, often at a specific time or location. Expressing phase as an angle maps one full cycle to 360° (or 2π radians). Selecting a consistent reference—such as the last negative-to-positive zero-crossing—ensures reproducible comparisons between signals. Step-by-Step Solution: Define phase as a fraction of the cycle (0 to 1) or in angular units (0° to 360°).Adopt a reference event: commonly the zero-up crossing in time-series.Note that degrees or radians are both valid; the key is consistency.All listed statements (A–C) are correct; hence choose “All of these.” Verification / Alternative check: Signal processing texts define phase in exactly these terms; interferometric SAR uses phase differences modulo 2π. Why Other Options Are Wrong: Insisting on radians only is unnecessary; degrees are equally valid in practice. Common Pitfalls: Mixing degree and radian units; failing to document the phase reference point, which can lead to misinterpretation. Final Answer: All of these

Question 10

In geographic information science, what does topology describe, and which properties are considered topological characteristics of spatial objects?

Accepted Answer

Introduction / Context: Topology in GIS focuses on the relationships between spatial features rather than their precise metric dimensions. It supports error detection (e.g., gaps/overlaps), network analysis, and robust spatial operations by encoding how objects relate in space irrespective of the map projection or coordinate system. Given Data / Assumptions: We consider vector data structures: points, lines, and polygons. Transformations may distort shapes metrically but preserve relationships. Scale changes (zooming in/out) do not change topological relations. Concept / Approach: Topological properties remain invariant under continuous transformations (stretching, bending) that do not tear or glue. In GIS, the principal topological relations include adjacency (which polygons share borders), containment (which features lie within others), and connectivity (how lines/nodes are connected). These relationships are critical for tasks like parcel management, hydrological tracing, and routing. Step-by-Step Solution: Identify the definition: topology concerns relational, coordinate-free characteristics.Recognize independence from scale—zooming does not change what touches what.List the three canonical relations: adjacency, containment, connectivity.Therefore, all statements A–C are correct; select “All of these.” Verification / Alternative check: GIS topology rules (e.g., “must not overlap,” “must be covered by boundary of”) implement these properties operationally in software. Why Other Options Are Wrong: Restricting topology to connectivity ignores essential polygon–polygon and polygon–point relations (adjacency, containment). Common Pitfalls: Confusing topology with geometry; assuming reprojection changes topology—proper map projections preserve topological relations. Final Answer: All of these

Question 11

In Global Positioning System (GPS) receiver design, which antenna type is the most widely used on satellites, handheld devices, and surveying rovers due to its compact form factor and right-hand circular polarization support?

Accepted Answer

Introduction / Context: GPS receivers must capture very weak L-band signals arriving from satellites at high altitudes. The antenna choice affects gain pattern, polarization, size, weight, and cost. In practice, the microstrip (patch) antenna has become the most widely used solution across consumer, professional, and even spaceborne applications because it balances radio-frequency performance with manufacturing practicality. Given Data / Assumptions: Operating in L-band (around L1 1575.42 MHz and L2 1227.60 MHz), right-hand circular polarization (RHCP) is required. Receivers are often size- and power-constrained (phones, drones, rovers). Omnidirectional or hemispherical patterns are desirable for sky coverage. Concept / Approach: Microstrip patch antennas are printed on dielectric substrates, enabling compact, low-profile designs. They are easily integrated, can be arrayed for higher gain, and can be designed for RHCP using simple feed networks or stacked patches. Compared with dishes or horns, patches offer adequate gain for GPS and are far easier to embed in mobile platforms. Step-by-Step Solution: Identify GPS antenna needs: RHCP, suitable hemispherical pattern, compactness.Evaluate candidates: parabolic and horn antennas provide high gain but are bulky and impractical for handhelds.Consider slotted or dipole designs: may need complex networks to achieve RHCP and desired patterns.Conclude microstrip patch optimally meets requirements and is the prevalent choice. Verification / Alternative check: Survey-grade choke-ring antennas use stacked microstrip patches with ground planes to mitigate multipath. Smartphones and receivers commonly feature ceramic patch or flexible printed patches, confirming widespread adoption. Why Other Options Are Wrong: Parabolic antenna: excellent gain but far too large and heavy for typical GPS receivers. Horn antenna: high directivity and size not suited for consumer GPS. Slotted antenna: possible but less common for mass GPS use. Dipole with balun: linear polarization and pattern limitations for GPS unless heavily engineered. Common Pitfalls: Assuming higher gain always equals better GPS performance; in practice, pattern uniformity, RHCP, and multipath rejection matter more than raw gain for many applications. Final Answer: Microstrip antenna

Question 12

Considering Earth’s radiation budget and diurnal cycle, which statements correctly describe how the Earth reflects and emits radiation during daytime and nighttime conditions?

Accepted Answer

Introduction / Context: Remote sensing relies on understanding sources of electromagnetic energy. Earth’s surface and atmosphere interact with incoming shortwave solar radiation and continuously emit longwave thermal radiation. Recognizing how these components vary throughout the day is essential when interpreting optical and thermal images. Given Data / Assumptions: Shortwave solar radiation dominates illumination in the visible and near-infrared during daytime. Earth’s surface emits thermal infrared radiation day and night based on temperature and emissivity. Clouds and atmosphere also reflect and emit radiation. Concept / Approach: During daytime, sensors detect a combination of reflected solar energy (shortwave) and emitted terrestrial energy (longwave). At night, the reflected component vanishes but thermal emission persists, enabling thermal imagers to operate without sunlight. Understanding this is crucial for selecting appropriate bands and interpreting radiance as reflectance or brightness temperature. Step-by-Step Solution: Daytime: confirm presence of reflected solar radiation from surfaces and clouds.Daytime: acknowledge concurrent thermal emission from surfaces (lower magnitude in shortwave, dominant in mid/thermal IR).Nighttime: only thermal emission remains detectable in passive measurements.Hence, all statements A–C are correct, so choose “All of these.” Verification / Alternative check: Visible images disappear at night; thermal IR images remain available, demonstrating continuous emission. Daytime thermal imagery records surface temperature patterns even under sunlight. Why Other Options Are Wrong: Claiming negligible daytime reflection contradicts the very basis of optical remote sensing. Common Pitfalls: Confusing radiance units with reflectance; ignoring atmospheric contributions to both reflection and emission. Final Answer: All of these

Question 13

When an electromagnetic wave encounters the interface between two lossless homogeneous media with different refractive indices, part of the energy is reflected back and part is transmitted. What is this boundary interaction known as?

Accepted Answer

Introduction / Context: At material interfaces, electromagnetic waves undergo reflection and transmission. The fractions of reflected and transmitted power depend on incidence angle, polarization, and refractive indices. These relations are foundational for optics, radar remote sensing, and radiative transfer. Given Data / Assumptions: Two lossless homogeneous media with different refractive indices. Plane wave incidence at an arbitrary angle. Linear polarization states (s and p) may be considered. Concept / Approach: The Fresnel equations quantify reflection and transmission coefficients for s- and p-polarized waves at a planar boundary. While Snell’s law governs the change in propagation direction for the transmitted wave, the specific description of partial reflection and transmission magnitudes is encapsulated by Fresnel reflection/transmission. Step-by-Step Solution: Identify the phenomenon: simultaneous reflection and transmission at an interface.Associate to the proper formalism: Fresnel equations define the reflectance/transmittance.Select the term that directly names the reflective component: “Fresnel reflection.” Verification / Alternative check: Optics texts show how reflectance varies with incidence angle and polarization; at Brewster’s angle, p-polarized reflectance goes to zero, illustrating Fresnel behavior. Why Other Options Are Wrong: Fresnel refraction describes the transmitted side, not the combined reflective phenomenon being asked. Snell’s law only gives refraction angles; it does not provide reflection coefficients. “None of these” ignores standard terminology. Common Pitfalls: Confusing direction (Snell) with amplitude/energy (Fresnel); forgetting polarization dependence. Final Answer: Fresnel reflection

Question 14

In optical remote sensing, stray scattered or diffused radiance from outside the target entering the sensor’s field of view most commonly has what effect on the resulting image?

Accepted Answer

Introduction / Context: Atmospheric scattering and sensor stray light add unwanted radiance to the signal that should represent only the target. This contamination acts like a veiling light (haze), diminishing the ability to distinguish bright and dark features and blurring fine detail. Given Data / Assumptions: Stray radiance originates from outside the intended target area. The effect is approximately additive in radiance. Sensor optics and atmosphere both contribute. Concept / Approach: An additive haze term raises dark pixel values toward mid-tones without equivalently lifting highlights, compressing dynamic range (contrast). Multiple scattering and optical flare also smear edges and dampen high-frequency content, lowering perceived sharpness and measurable modulation transfer. Step-by-Step Solution: Model observed radiance: L_obs = L_target + L_haze.As L_haze grows, differences between dark and mid-tones shrink → reduced contrast.Stray light spreads energy across neighboring pixels → edge softening → reduced sharpness.Therefore, the correct effect is decreased contrast and decreased sharpness. Verification / Alternative check: Haze removal algorithms (dark object subtraction, atmospheric correction) explicitly reduce an estimated additive path-radiance term to restore contrast and some detail. Why Other Options Are Wrong: Increasing contrast is inconsistent with additive haze. Sharpness generally does not improve in the presence of stray light. Common Pitfalls: Confusing random sensor noise with systematic veiling glare; misinterpreting contrast changes after histogram stretching as true contrast recovery. Final Answer: Reduces the contrast of the image and also its sharpness

Question 15

In remote sensing interpretation, which concept specifically refers to the unique combination of spectral, spatial, temporal, and polarimetric responses that enables identification of Earth surface objects?

Accepted Answer

Introduction / Context: Accurate identification of land-cover and objects in imagery depends on recognizing characteristic response patterns. These patterns arise from material properties, structure, and context and form the “signature” that analysts and algorithms use for classification. Given Data / Assumptions: Sensors measure reflectance/emittance and sometimes polarization or phase. Targets include vegetation, soils, water, snow/ice, and man-made materials. Atmospheric effects are corrected or minimized to reveal target traits. Concept / Approach: A target’s signature spans multiple dimensions: spectral (multi-band reflectance/emittance), spatial (shape, texture), temporal (seasonal or event-driven change), and polarimetric (response to polarization, especially in SAR). Classifiers learn or reference these signatures to distinguish between similar-looking classes. Step-by-Step Solution: Define “signature” as the distinctive response pattern of a target.Differentiate from “atmospheric window,” which relates to transmission bands, not identification.Exclude “radiometric error,” which is an unwanted measurement artifact.Therefore, the correct choice is “Signature.” Verification / Alternative check: Vegetation signatures show strong red absorption and near-IR reflectance; water shows low near-IR reflectance; built-up areas show unique spectral-textural combinations. Why Other Options Are Wrong: Atmospheric window: describes transmissive spectral regions of the atmosphere. Radiometric error: reduces data quality, not an identification tool. None of these/geoid: unrelated to image object identification. Common Pitfalls: Relying on spectral cues alone and ignoring spatial or temporal components of a signature; overlooking polarization in SAR data. Final Answer: Signature

Question 16

Choose the correct statements about waves and phase: definitions of a wavefront, a plane wave, and the importance of relative phase between waves.

Accepted Answer

Introduction / Context: Wave theory underpins optics, radar, acoustics, and remote sensing. Understanding wavefronts, plane waves, and phase relationships enables interpretation of coherence, interference, and imaging performance. Given Data / Assumptions: Monochromatic waves are considered for simplicity. Phase is defined modulo 2π and can be mapped across space. Media are assumed homogeneous for the plane-wave concept. Concept / Approach: A wavefront connects points of equal phase, such as crests in a harmonic wave. If these wavefronts are planar and equally spaced, the wave is a plane wave, an idealization widely used in analysis. Interference patterns depend on phase differences between waves; shifting both waves by the same absolute phase does not alter measurable outcomes, hence the primacy of relative phase. Step-by-Step Solution: Confirm definition of wavefront as constant-phase surface.Identify plane waves by parallel plane wavefronts.Recognize interference depends on phase difference, not absolute phase.Therefore, all statements are correct, so choose “All of these.” Verification / Alternative check: Huygens–Fresnel principle and Fourier optics rely on wavefront concepts; interferometry (including SAR) measures relative phase to infer geometry. Why Other Options Are Wrong: Limiting correctness to only two statements ignores the central role of relative phase in interference. Common Pitfalls: Confusing rays (energy flow directions) with wavefronts (phase surfaces); over-interpreting absolute phase which is typically unobservable. Final Answer: All of these

Question 17

For a standard sinusoidal wave referenced at a zero-up crossing, what are the typical phase values for a crest, the next zero crossing, and the following trough along the wave?

Accepted Answer

Introduction / Context: Phase labeling of a sinusoid is fundamental in signal processing, optics, and communications. Establishing a consistent reference (such as the previous negative-to-positive zero crossing) allows us to assign standard phase angles to notable points on the waveform. Given Data / Assumptions: A unit-amplitude sinusoid is considered. The phase reference is taken at the zero crossing where the wave passes upward through zero (zero-up crossing). Phase increases in the direction of propagation. Concept / Approach: For a cosine-like reference shifted to start at zero-up crossing, the crest occurs a quarter cycle later (π/2), the next zero crossing (downward) occurs at half a cycle (π), and the trough appears at three-quarters of a cycle (3π/2). These canonical phase points are frequently used in phasor diagrams and time-series analysis. Step-by-Step Solution: Set the phase origin at zero-up crossing (phase 0).Advance by a quarter cycle → crest at π/2.Advance by another quarter cycle → downward zero crossing at π.Advance by another quarter cycle → trough at 3π/2.Thus, all listed phase assignments are correct. Verification / Alternative check: Plotting a sine wave with phase origin at zero-up crossing reproduces these angles exactly. Why Other Options Are Wrong: Assigning crest at π or trough at 2π misplaces features by half a cycle or a full cycle. Common Pitfalls: Mixing sine and cosine phase references; forgetting to specify the reference crossing (upward vs. downward). Final Answer: All of these

Question 18

According to quantum theory applicable to electromagnetic radiation, the energy of a single photon (energy quantum) is proportional to which quantity?

Accepted Answer

Introduction / Context: Photon energy connects wave and particle descriptions of electromagnetic radiation. In remote sensing, detector response and atmospheric processes depend on photon energy as well as radiance, making the proportionality between energy and frequency foundational. Given Data / Assumptions: Planck’s relation applies to photons: E = h * f. h is Planck’s constant; f is frequency; λ is wavelength; c = f * λ. We consider single-photon energy, not macroscopic radiant energy. Concept / Approach: Planck’s relation states that photon energy is directly proportional to frequency and inversely proportional to wavelength (E = h * f = h * c / λ). Higher frequency (e.g., UV) means higher photon energy; lower frequency (e.g., microwave) means lower photon energy. This governs interactions like electronic transitions and thermal emission. Step-by-Step Solution: Recall E = h * f.Identify that “proportional to frequency” matches the formula.Select the option: “The frequency.” Verification / Alternative check: Spectral bands with shorter wavelengths (higher frequencies) carry higher energy photons, explaining stronger photochemical effects in UV compared to IR. Why Other Options Are Wrong: Square, square root, or reciprocal relationships do not reflect Planck’s law. Energy is inversely—not directly—proportional to wavelength. Common Pitfalls: Confusing photon energy with power (energy per unit time); mixing up wavelength and frequency relationships. Final Answer: The frequency

Question 19

Within a Geographic Information System (GIS), which definitions correctly describe core topological relationships among spatial features?

Accepted Answer

Introduction / Context: Topology captures the relationships among GIS features independent of their exact coordinates or scale. It enables quality control (e.g., no gaps between polygons), network analysis (e.g., routing), and spatial reasoning. Given Data / Assumptions: Vector data model with points, lines, and polygons. Topology is invariant under continuous transformations that do not tear or glue features. Definitions focus on relationships, not metric distances. Concept / Approach: Adjacency describes which polygons share boundaries; containment identifies features wholly within others; connectivity encodes how lines join at nodes. Together these relations support tasks such as parcel adjacency checks, hydrologic tracing, and utility network flow analysis. Step-by-Step Solution: Map each term to its canonical GIS meaning.Recognize all three are topological, not purely geometric/metric.Conclude the inclusive option “All of these” is correct. Verification / Alternative check: Topology rules in GIS software enforce adjacency/containment/connection constraints during editing and validation. Why Other Options Are Wrong: Claiming only connectivity matters ignores essential polygon-polygon relations fundamental to GIS topology. Common Pitfalls: Confusing geometric accuracy (e.g., coordinate precision) with topological correctness (e.g., shared boundaries). Final Answer: All of these.

Question 20

Which set of parameters correctly summarizes key characteristics of GPS satellites in the NAVSTAR constellation (medium Earth orbit, plane inclination, and transmitted L-band signals)?

Accepted Answer

Introduction / Context: GPS (NAVSTAR) satellites operate in medium Earth orbit (MEO) and broadcast precise timing and navigation signals on multiple L-band frequencies. Knowing their altitude, inclination, and signal frequencies helps practitioners understand coverage, dilution of precision, and receiver compatibility. Given Data / Assumptions: Constellation uses ~20,200 km circular MEO orbits. Orbital inclination is approximately 55 degrees. Legacy civil signals are on L1 (1575.42 MHz) and L2 (1227.60 MHz), with newer signals on L2C and L5. Concept / Approach: Altitude determines orbital period (~12 hours sidereal) and coverage footprint. Inclination affects latitude reach and satellite sky geometry. Multiple L-band signals allow dual-frequency ionospheric correction and improved robustness for precision applications. Step-by-Step Solution: Select the MEO altitude closest to 20,200 km.Confirm the standard GPS inclination near 55°.Identify the canonical L1 and L2 center frequencies: 1575.42 and 1227.60 MHz.Choose the option that lists all three correctly. Verification / Alternative check: GPS Interface Control Documents (ICDs) and satellite almanacs list these parameters; field receivers routinely use L1/L2 for dual-frequency positioning. Why Other Options Are Wrong: Geostationary altitude (~36,000 km) and 0° inclination do not describe GPS MEO satellites. Inclinations like 45° or polar sun-sync (98°) are not applicable to GPS. Signal sets limited to one band or other bands (e.g., S-band) do not match GPS. Common Pitfalls: Confusing GPS with augmentation systems (e.g., SBAS on GEO) or with other GNSS that use similar but not identical parameters. Final Answer: Nominal altitude about 20,200 km; orbital inclination ~55°; L1 at 1575.42 MHz and L2 at 1227.60 MHz

Elements of Remote Sensing Questions